This is the official codebase for the paper "Optimal transport with f-divergence regularization and generalized Sinkhorn algorithm" by Dávid Terjék and Diego González-Sánchez accepted for publication at the 25th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022.
Before running the experiments, prepare a python 3 environment with the following packages:
- argparse
- imageio
- matplotlib
- numpy
- tensorboard
- torch
And download the data from https://github.com/jeanfeydy/global-divergences/tree/master/sinkhorn_entropies/data
To run the experiments, execute
python fot_experiment.py --log_dir <path to a directory where plots will be saved> --data_dir <path to a directory where the supplied data (.png files) can be found>
The following parameters can be used for configuration:
--random_seed <integer: fixes the seed of random number generation, influences pointcloud sampling from the data>--mu_size <integer: number of points in the red pointcloud>--nu_size <integer: number of points in the blue pointcloud>--tolerance <float: convergence of Sinkhorn is assumed when this tolerance level is reached>--epsilon <float: etropic regularization coefficient>--dataset <string: one of "moons", "densities", "slopes", "crescents">--divergence <string: one of "kl", "reverse_kl", "chi2", "reverse_chi2", "hellinger2", "js", "jeffreys", "triangular">--double or --float: sets single or double precision